Scalable Retrieval of Similar Landscapes in Optical Satellite Imagery Using Unsupervised Representation Learning

Global Earth Observation (EO) is becoming increasingly important in understanding and addressing critical aspects of life on our planet about environmental issues, natural disasters, sustainable development and others. EO plays a key role in making informed decisions on applying or reforming land use, responding to disasters, shaping climate adaptation policies etc. EO is also becoming a useful tool for helping professionals make the most profitable decisions, e.g., in real estate or the investment sector. Finding similarities in landscapes may provide useful information regarding applying contiguous policies, taking alike decisions or learning from best practices on events and happenings that have already occurred in similar landscapes in the past. However, current applications of similar landscape retrieval are limited by moderate performance and the need for time-consuming and costly annotations. We propose splitting the similar landscape retrieval task into a set of smaller tasks that aim at identifying individual concepts inherent to satellite images. Our approach relies on several models trained with Unsupervised Representation Learning (URL) on Google Earth images to identify these concepts. We show the efficacy of matching individual concepts for tackling the task of retrieving similar landscape(s) to a user-selected satellite image with a proof-of-concept application of the proposed approach on the geographical territory of the Republic of Cyprus. Our results demonstrate the efficacy of breaking up the landscape similarity task into individual concepts closely related to remote sensing instead of trying to capture all concepts and image semantics with a single model like a single RGB semantics model

Scalable Retrieval of Similar Landscapes in Optical Satellite Imagery Using Unsupervised Representation Learning

Global Earth observation is becoming increasingly important in understanding and addressing critical aspects of life on our planet, including environmental issues, natural disasters, sustainable development, and others. Finding similarities in landscapes may provide useful information regarding applying contiguous policies, by making similar decisions or learning from best practices for events and occurrences that previously occurred in similar landscapes in the past. However, current applications of similar landscape retrieval are limited by a moderate performance and the need for time-consuming and costly annotations. We propose splitting the similar landscape retrieval task into a set of smaller tasks that aim to identify individual concepts inherent to satellite images. Our approach relies on several models trained using unsupervised representation learning on Google Earth images to identify these concepts. We show the efficacy of matching individual concepts for retrieving landscape(s) similar to a user-selected satellite image of the geographical territory of the Republic of Cyprus. Our results demonstrate the benefits of breaking up the landscape similarity task into individual concepts closely related to remote sensing, instead of applying a single model targeting all underlying concepts.</p

Change detection and landscape similarity comparison using computer vision methods

Human-induced disturbances of terrestrial and aquatic ecosystems continue at alarming rates. With the advent of both raw sensor and analysis-ready datasets, the need to monitor ecosystem disturbances is now more imperative than ever; yet the task is becoming increasingly complex with increasing sources and varieties of earth observation data. In this research, computer vision methods and tools are interrogated to understand their capability for comparing spatial patterns. A critical survey of literature provides evidence that computer vision methods are relatively robust to scale and highlights issues involved in parameterization of computer vision models for characterizing significant pattern information in a geographic context. Utilizing two widely used pattern indices to compare spatial patterns in simulated and real-world datasets revealed their potential to detect subtle changes in spatial patterns which would not otherwise be feasible using traditional pixel-level techniques. A texture-based CNN model was developed to extract spatially relevant information for landscape similarity comparison; the CNN feature maps proved to be effective in distinguishing agriculture landscapes from other landscape types (e.g., forest and mountainous landscapes). For real-world human disturbance monitoring, a U-Net CNN was developed and compared with a random forest model. Both modeling frameworks exhibit promising potential to map placer mining disturbance; however, random forests proved simple to train and deploy for placer mapping, while the U-Net may be used to augment RF as it is capable of reducing misclassification errors and will benefit from increasing availability of detailed training data